| Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects E Dumitrescu, S Hué, C Hurlin, S Tokpavi European Journal of Operational Research 297 (3), 1178-1192, 2022 | 219 | 2022 |
| Measuring network systemic risk contributions: A leave-one-out approach S Hué, Y Lucotte, S Tokpavi Journal of Economic Dynamics and Control 100, 86-114, 2019 | 50 | 2019 |
| Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds EI Dumitrescu, S Hué, C Hurlin | 28 | 2021 |
| Measuring the driving forces of predictive performance: Application to credit scoring S Hué, C Hurlin, C Pérignon, S Saurin HEC Paris Research Paper No. FIN-2022-1463, 2023 | 1 | 2023 |
| Explainable performance S Hué, C Hurlin, C Pérignon, S Saurin HAL Working Papers, 2022 | 1 | 2022 |
| GAM (L) A: An econometric model for interpretable Machine Learning E Flachaire, G Hacheme, S Hué, S Laurent arXiv preprint arXiv:2203.11691, 2022 | 1 | 2022 |
| Interpretable Machine Learning Using Partial Linear Models E Flachaire, S Hué, S Laurent, G Hacheme Oxford Bulletin of Economics and Statistics, 2023 | | 2023 |
| GAM (L) A: An econometric model for interpretable machine learning S Hué French Stata Users' Group Meetings 2022, 2022 | | 2022 |
| Granger-Causality in Quantiles and Financial Interconnectedness J Leymarie, S Hué Available at SSRN 4074763, 2022 | | 2022 |
Christophe HurlinProfessor of Economics, University of OrleansПотвърден имейл адрес: univ-orleans.fr
